ci: run the tutorials on PyTorch 2.15 and fix the ADA notebook

.github/workflows/build.yml

@@ -21,8 +21,8 @@ jobs:
       fail-fast: false
       matrix:
         kornia-ref: ['main'] # TODO: coverage check through multiple versions: , 'v0.7.1', 'v0.7.0']
-        pytorch-version: ['2.14.0']
-        torchvision-version: ['0.29.0']
+        pytorch-version: ['2.15.0']
+        torchvision-version: ['0.29.2']
 
     steps:
     - uses: kornia/workflows/.github/actions/env@v1.13.0
@@ -66,8 +66,8 @@ jobs:
       matrix:
         os: ['ubuntu']
         kornia-ref: ['main']
-        pytorch-version: ['2.14.0']
-        torchvision-version: ['0.29.0']
+        pytorch-version: ['2.15.0']
+        torchvision-version: ['0.29.2']
 
     steps:
     - uses: kornia/workflows/.github/actions/env@v1.13.0

nbs/adaptive_discriminator_augmentation_preset.ipynb

@@ -165,6 +165,9 @@
    "outputs": [],
    "source": [
     "panda_image = kornia.image_to_tensor(np.array(Image.open(\"panda.jpg\").convert(\"RGB\"))).float() / 255.0  # C, H, W\n",
+    "# the image is wider than tall: keep its central square, see the note on the default augmentations below\n",
+    "side = min(panda_image.shape[-2:])\n",
+    "panda_image = K.CenterCrop((side, side))(panda_image)[0]\n",
     "panda_images = panda_image.unsqueeze(0).repeat(8, 1, 1, 1)  # 8, C, H, W\n",
     "\n",
     "\n",
@@ -217,7 +220,9 @@
     "id": "exUbjcCQX2Q4"
    },
    "source": [
-    "we can use the `AdaptiveDiscriminatorAugmentation` API with the default list of augmentations or using a custom list, starting with the default ones in this example"
+    "we can use the `AdaptiveDiscriminatorAugmentation` API with the default list of augmentations or using a custom list, starting with the default ones in this example\n",
+    "\n",
+    "**note:** the default list contains `RandomRotation90(times=(0, 3))`, which raises a `ValueError` on a non-square image because an odd number of quarter turns would cut part of it off, so the default list needs square images"
    ]
   },
   {
@@ -492,7 +497,8 @@
     "- adjustment speed is set to a slightly high value\n",
     "- `p` is updated each step\n",
     "- target real accuracy for the *Discriminator* to maintain is 0.6\n",
-    "- the exponentially moving average lambda set to `0.5` to have an observable evolution of `p` over relatively few steps"
+    "- the exponentially moving average lambda set to `0.5` to have an observable evolution of `p` over relatively few steps\n",
+    "- the images are 64 x 128, not square, so instead of the default augmentations we pass the same list with half turns in place of quarter turns"
    ]
   },
   {
@@ -510,7 +516,12 @@
    "outputs": [],
    "source": [
     "ada = AdaptiveDiscriminatorAugmentation(\n",
-    "    # you can pass the custom augmentations you want to use here,\n",
+    "    K.RandomHorizontalFlip(p=1.0),\n",
+    "    K.RandomRotation90(times=(2, 2), p=0.5),\n",
+    "    K.RandomErasing(scale=(0.02, 0.33), ratio=(0.3, 3.3), p=0.9),\n",
+    "    K.RandomAffine(degrees=10, translate=(0.1, 0.1), scale=(0.9, 1.1), p=1.0),\n",
+    "    K.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1, p=1.0),\n",
+    "    K.RandomGaussianNoise(std=0.1, p=1.0),\n",
     "    adjustment_speed=0.05,\n",
     "    update_every=1,\n",
     "    target_real_acc=0.6,\n",

requirements.txt

@@ -16,7 +16,7 @@ pytorch_lightning
 rawpy
 ray[data]
 tensorboard
-torch>=2.14.0
+torch>=2.15.0
 torchmetrics
-torchvision>=0.29.0
+torchvision>=0.29.2
 tqdm